基于BiLSTM神经网络的锂电池SOH估计与RUL预测

针对锂电池健康状态(SOH)估计与剩余寿命(RUL)预测问题,设计一种基于双向长短期记忆(BiLSTM)神经网络模型的预测方法.首先,提取美国国家航空航天局(NASA)锂电池的容量数据,将容量数据转为SOH数据并作为模型输入数据.其次,建立双层BiLSTM神经网络,使用加速自适应矩估计算法(Nadam)优化函数动态调整...

The SOH estimation and RUL prediction of lithium battery based on BiLSTM

This paper uses a bi-directional long short-term memory (BiLSTM) neural network model to solve the state of health (SOH) and remaining useful life (RUL) traditional prediction methods of lithium batteries. The accuracy of traditional prediction methods is low. The problem. Firstly, extract the capacity data of the national aeronautics and space administration (NASA) lithium battery, convert the capacity data into SOH data and use it as input data; secondly, establish a two-layer BiLSTM neural network and use the Nadam optimization function to dynamically adjust learning rate; Then, the lithium battery data is analyzed through the two-way long and short-term memory neural network model to establish the connection between battery capacity, SOH and RUL; finally, the fully connected layer outputs the estimated curve of the battery SOH to predict its remaining life. Prediction experiments with NASA data show that the RUL prediction error of the BiLSTM neural network is stable within 3, and the fit of the SOH prediction curve is stable at 94.211%-95.839%. The BiLSTM neural network has higher robustness and accuracy.